Another popular technique for classification (or at least, which used to be popular) is the (linear) discriminant analysis, introduced by Ronald Fisher in 1936. Consider the same dataset as in our previous post

> clr1 <- c(rgb(1,0,0,1),rgb(0,0,1,1))
> x <- c(.4,.55,.65,.9,.1,.35,.5,.15,.2,.85)
> y <- c(.85,.95,.8,.87,.5,.55,.5,.2,.1,.3)
> z <- c(1,1,1,1,1,0,0,1,0,0)
> df <- data.frame(x,y,z)
> plot(x,y,pch=19,cex=2,col=clr1[z+1])

The main interest of that technique is not the output, but more the fact that we can make here simple (and explicit) computations. Especially to get a better understanding of theoretical concepts on classification.

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